Version 1
: Received: 11 March 2024 / Approved: 12 March 2024 / Online: 14 March 2024 (05:53:47 CET)
How to cite:
Alhagyan, M. Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM. Preprints2024, 2024030699. https://doi.org/10.20944/preprints202403.0699.v1
Alhagyan, M. Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM. Preprints 2024, 2024030699. https://doi.org/10.20944/preprints202403.0699.v1
Alhagyan, M. Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM. Preprints2024, 2024030699. https://doi.org/10.20944/preprints202403.0699.v1
APA Style
Alhagyan, M. (2024). Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM. Preprints. https://doi.org/10.20944/preprints202403.0699.v1
Chicago/Turabian Style
Alhagyan, M. 2024 "Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM" Preprints. https://doi.org/10.20944/preprints202403.0699.v1
Abstract
The future index prices are viewed as a critical issue for any trader and investor. For this craving, various models have shown up in the literature. The Geometric Brownian motion (GBM) is one of the popular models. This work examines four types of GBM as per the presence of memory or kind of volatility. These models include classical GBM with memoryless and constant volatility assumptions, SVGBM with memoryless and stochastic volatility assumption, GFBM with memory and constant volatility assumption, and SVGFBM with memory and stochastic volatility assumption. These models are utilized in an empirical study to forecast the future index price of Energy Sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, mean square error (MSE) and mean absolute percentage error (MAPE). The outcomes showed that the SVGFBM occupies the highest level of accuracy according to smallest values of MSE and MAPE. While the accuracy of GBM come in the tail of the list models under study. These results have affirmed the positive affection of combining memory and stochastic volatility assumption into the GBM model, which agreed findings of numerous earlier works. Furthermore, the findings showed the GFBM models are more accurate than GBM models regardless of the type of volatility. While, under the same type of memory, the models with stochastic volatility assumption are more accurate than the corresponding models of constant volatility assumption. In general, all models considered in this work uncovered high accuracy through the value of MAPE ≤ 10%. This indicates of the ability of applying these models in a real financial environment. Through this empirical study, we can say that the Energy Sector in Saudi Arabia is predictable and stable and afterward we urge financial investors and stockholder to trade and invest in this sector.
Keywords
Energy sector; GBM; GFBM; stochastic volatility
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
